Valuing the EQ-5D and the SF-6D health states using subjective well-being: A secondary analysis of patient data Clara Mukuria * , John Brazier Health Economics and Decision Science, ScHARR, University of Sheffield, Sheffield, UK article info Article history: Available online 19 November 2012 Keywords: Wales United Kingdom EQ-5D SF-6D Health state valuation Preferences Utility Subjective well-being Happiness abstract The economic evaluation of health care technologies employs a standard economic approach based on preferences to provide utility information. Previous studies have used happiness rather than preferences to weight health states using general population data. However, these data may not reflect the full range and scope of health and happiness experienced by patients. This paper applies a similar approach to a large patient sample (N ¼ 15,184) from a hospital in Wales, UK collected between 2002 and 2004. Logit regression models were used to assess the relationship between happiness and the health state classi- fications of two measures, the EQ-5D and the SF-6D. The results suggest a different weighting across dimensions to that from preference elicitation techniques such as time trade-off and standard gamble. While mental health (depression and anxiety), vitality and social functioning were found to have a large significant association with the patients’ own happiness assessment, pain was less so and physical health had no association. Ó 2012 Elsevier Ltd. All rights reserved. Introduction Background There has been an increased interest in measuring subjective well-being (SWB) to inform public policy in the United Kingdom as well as in other countries (Abdallah et al., 2011; Forgeard, Jayawickreme, Kern, & Seligman, 2011; Waldron, 2010). This has been driven in part by evidence that increase over time in measures of material well-being such as gross national income has not been matched by increase in SWB. As one of the aims of public policy is to increase overall well-being, including both material and subjective aspects of well-being measures to measure progress and inform public policy decisions is important. In neo-classical economics, income has been used as a proxy for well-being or utility as higher income provides individuals with opportunities to choose more goods or services. Individuals are assumed to be utility maximisers and choices indicate preferences for goods or services that will increase their well-being. This is in contrast to the approach taken by classical economists such as Jeremy Bentham who proposed measuring the well-being of an object based on its ability to increase pleasure or happiness, or to reduce pain which is directly related to SWB (Bentham, 1781/2000; Layard, 2005). Perceived problems with measuring SWB led to the move away from this definition of utility and towards preferences. Kahneman distinguishes between the two forms of utility by referring to the former as decision utility and the latter as experi- ence utility (Kahneman, 2000). Health care policy makers rely on information from the economic evaluation of health care technologies to make resource allocation decisions. Cost utility analysis is an economic evaluation technique which uses the Quality Adjusted Life Year (QALY) to measure the health effects of conditions and associated medical interventions. The QALY is estimated by weighting survival with the health related quality of life (HRQoL) enjoyed in each time period using health state utility values (Torrance & Feeny, 1989). The health state utility values are obtained using preference based HRQoL measures, such as the EQ-5D, SF-6D and the Health Utilities Index (HUI 2 and HUI 3). These measures have a health state clas- sification (HSC) describing health states typically in terms of physical, mental, role and social functioning which is completed by patients. The completed HSC is then scored using existing values obtained via preference elicitation techniques such as the standard gamble (SG) and time trade-off (TTO). SG and TTO aim to elicit utility values associated with hypothetical health states by asking individuals to trade in uncertainty or healthy life years. The values obtained represent preferences or decision utility for hypothetical health states. These values are obtained from members of the * Corresponding author. Tel.: þ44 114 222 6395. E-mail address: c.mukuria@sheffield.ac.uk (C. Mukuria). Contents lists available at SciVerse ScienceDirect Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed 0277-9536/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.socscimed.2012.11.012 Social Science & Medicine 77 (2013) 97e105